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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNER) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNER accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNER benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

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Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite system(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite system(BDS) long short-term memory(LSTM) multipath classification

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Glass catfish inspired subaquatic abrasion-resistant anti-fouling window fabricated by femtosecond laser electrodeposition

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International Journal of Extreme Manufacturing 2025年第1期7卷 383-393页

作者： Jialiang Zhang Fangzheng Ren Qing Yang Qingyun Ma Jie Liang Yizhao Meng Xiaodan Gou Chongxiao Xia Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi’an Jiao Tong UniversityXi’an 710049People’s Republic of China School of Instrument Science and Technology Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China

Transparent materials utilized as underwater optical windows are highly vulnerable to various forms of pollution or abrasion due to their intrinsic hydrophilic *** susceptibility is particularly pronounced in underwater environments where pollutants can impede the operation of these optical devices,significantly degrading or even compromising their optical *** glass catfish,known for its remarkable transparency in water,maintains surface cleanliness and clarity despite exposure to contaminants,impurities abrasion,and hydraulic *** by the glass catfish’s natural attributes,this study introduces a new solution named subaquatic abrasion-resistant and anti-fouling window(SAAW).Utilizing femtosecond laser ablation and electrodeposition,the SAAW is engineered by embedding fine metal bone structures into a transparent substrate and anti-fouling sliding layer,akin to the sturdy bones among catfish’s *** approach significantly bolsters the window’s abrasion resistance and anti-fouling performance while maintaining high light *** sliding layer on the SAAW’s surface remarkably reduces the friction of various liquids,which is the reason that SAAW owns the great anti-fouling *** SAAW demonstrates outstanding optical clarity even after enduring hundreds of sandpaper abrasions,attributing to the fine metal bone structures bearing all external forces and protecting the sliding layer of ***,it exhibits exceptional resistance to biological adhesion and underwater *** a green algae environment,the window remains clean with minimal change in transmittance over one ***,it retains its wettability and anti-fouling properties when subjected to a depth of 30 m of underwater pressure for 30 ***,the SAAW prepared by femtosecond laser ablation and electrodeposition presents a promising strategy for developing stable optical windows in liquid environments.

关键词： anti-fouling femtosecond laser subaquatic window slippery surface abrasion resistance

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Joint Design of ISAC Waveform Under PAPR Constraints

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China Communications 2024年第7期21卷 186-211页

作者： Chen Yating Wen Cai Huang Yan Liang Le Li Jie Zhang Hui Hong Wei State Key Lab of Millimeter Waves Southeast UniversityNanjing 211100China School of Information Science and Technology Northwest UniversityXi’an 710127China National Mobile Communications Research Laboratory Nanjing 211100China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211100China

In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)*** propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each *** addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of *** limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function *** results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.

关键词： ambiguity function integrated sensing and communication MIMO OFDM PAPR waveform design

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Efects of Defect,Mean Stress and Lower Loading on High Cycle and Very High Cycle Fatigue Behavior of Ti‑6Al‑4V Alloy

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Acta Metallurgica Sinica(English Letters) 2025年第3期38卷 435-448页

作者： Yiyun Guo Lei Wu Yibo Shang Chengqi Sun State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China Guobiao(Beijing)Testing&Certifcation Co. Ltd.Beijing 101400China Fundamentals Department Air Force Engineering UniversityXi’an 710051China National Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering UniversityXi’an 710038China

In this study,the efects of defect,mean stress and lower loading are investigated for high cycle(HCF)and very high cycle fatigue(VHCF)behavior of Ti-6Al-4V *** indicates that the S-N curve of Ti-6Al-4V alloy exhibits a linear decreasing trend or a plateau characteristic in HCF and VHCF regimes,which depends on the defect size and stress *** strength decreases with increasing the defect size,and it is irrespective of stress *** fatigue crack initiates from specimen surface at R=−1 in both HCF and VHCF *** the fatigue crack initiates from the subsurface or the interior of the specimen at R=0.1 in VHCF regime.A sequence of lower stress amplitude below the fatigue strength at 10^(9) cyc has no or negligible infuence on the fatigue life of 10^(5)-10^(9) *** lower stress amplitude in variable amplitude loadings does not afect the failure *** residual compressive stress relaxation is not observed after a large number of lower loadings under ultrasonic frequency fatigue *** formula and Goodman formula give dangerous predictions of VHCF strength for both smooth specimens and specimens with defects.

关键词： Ti-6Al-4V titanium alloy Very high cycle fatigue Defect Stress ratio Lower stress amplitude

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure information extraction Heuristics Cell function analysis Table structure analysis

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Transfer Learning in Grape Disease Leaf Detection Based on Convolutional Neural Network 4

Transfer Learning in Grape Disease Leaf Detection Based on C...

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4th International Seminar on Artificial Intelligence, Networking and information technology, AINIT 2023

作者： Li, Yize Liu, Zhe Jiang, Yuxin Toe, Teoh Teik University of Electronic Science and Technology of China Optoelectronic Science and Engineering Chendu China University of Electronic Science and Technology of China Automation Engineering Chendu China University of Electronic Science and Technology of China Information and Communication Engineering Chendu China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9798350314526

In order to achieve rapid and accurate recognition of grape leaf disease images, this paper introduces a convolutional neural network model based on transfer learning for classifying diseased grape leaves. A new fully connected layer module was designed on the basis of the EfficientNetB0 model, and the convolutional layer of the EfficientNetB0 model, pre-Trained on the ImageNet dataset, was transferred into this model. The training image data of thousands of images were obtained from Kaggle, including grape leaves with black rot disease, Esca disease virus, leaf blight disease, and healthy grape leaves. In order to expand the dataset and prevent overfitting, we carried out a series of preprocessing steps on the original dataset and divided the training and test sets in a 4:1 ratio. The test accuracy of our model reached 99.14% and the average F1-score reached 98.79%. This paper also compared the classification results of different model structures such as VGG-16 and RESNet50. Their test accuracy values are 96.29% and 97.06% respectively. To quantitatively evaluate the performance of the model, the accuracy, precision, recall and F1-socre of the model are calculated. © 2023 IEEE.

关键词： Convolutional neural networks

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